https://orcid.org/0000-0001-5071-1423
Figures
Abstract
Background
Adherence to colorectal cancer (CRC) screening remains suboptimal in many countries, reducing its cost-effectiveness. This study aimed to evaluate how multistage uptake rates influence the health benefit and cost-effectiveness of various CRC screening strategies in the Chinese population, incorporating both traditional and emerging screening methods.
Methods and findings
We developed a multistate Markov model (CRC-SIM) to evaluate the impact of multistep uptake on CRC screening. A hypothetical cohort of 100,000 individuals aged 40 was simulated and followed until 79 or death. Two-step screening strategies were modeled: initial screening followed by colonoscopy after a positive result. Traditional initial screening methods include: questionnaire-based risk assessment, fecal immunochemical test (FIT), and questionnaire combined with FIT; Non-invasive biomarker-based initial strategies include a hypothetical test meeting the minimum standards of China National Medical Products Administration (NMPAmin), multitarget stool DNA (mt-sDNA) test, and blood-based strategies. All strategies were modeled as one-time screenings, with outcomes projected for CRC cases, deaths, quality-adjusted life years (QALYs), and lifetime costs. Incremental cost-effectiveness ratios (ICERs) were calculated, and a cost-effectiveness heatmap was conducted to assess the impact of multistep uptake (modeled in 10% steps) on economic outcomes. All strategies reduced CRC cases, deaths and increased QALYs compared to no screening, with biomarker-based strategies outperforming the traditional methods at the same uptake level (e.g., questionnaire combined with FIT prevented 224 (95% confidence interval (CI) [157, 292]) CRC cases and 151 (95% CI [109, 195]) deaths, whereas NMPAmin prevented 312 (95% CI [257, 360]) cases and 210 (95% CI [175, 241]) deaths at 100% uptake). The cost-effectiveness heatmap indicated that each 10% increase in initial and follow-up colonoscopy uptake improved ICERs in a non-linear pattern. The questionnaire combined with FIT was the most cost-effective strategy (ICER = $2,413 per QALY gained). Non-invasive biomarker-based tests were not cost-effective compared with the combined questionnaire and FIT strategy under current assumptions of test costs and identical uptake rate. Threshold analysis showed that non-invasive biomarker-based screening would become cost-effective if test costs fell below $131.7 or colonoscopy uptake increased to at least 70% for NMPAmin and 50% for blood-based tests and mt-sDNA. Limitations include the assumption of a one-time screening scenario; future iterations of the model and merging evidence in repeated screening will address these limitations.
Conclusion
Improving screening participation could enhance health benefits and cost-efficiency in CRC screening. Questionnaire-based risk assessment combined with FIT was a cost-effective strategy in China, whereas non-invasive biomarker-based methods require cost reduction and higher uptake to justify adoption. These findings provide evidence for policymakers to optimize CRC screening programs.
Author summary
Why was this study done?
- The burden of colorectal cancer (CRC) in China has been growing in recent years.
- Suboptimal adherence rates compromise the health benefit and the cost-effectiveness of CRC screening.
- The quantitative relationship between multistage adherence in CRC screening and its long-term health and cost-effectiveness benefit remains insufficiently evaluated.
What did the researchers do and find?
- Leveraging a well-validated multistate Markov model, we quantified the impact of multistage adherence on the long-term health and economic benefit, incorporating both traditional and emerging biomarker-based initial screening modalities.
- Our analysis demonstrated that enhancing adherence rate at both the initial risk assessment and follow-up colonoscopy stages yields substantial health benefits and cost-effectiveness gains.
- Questionnaire-based risk assessment combined with fecal immunochemical test was a cost-effective strategy in China, while non-invasive biomarker-based tests were not cost-effective unless costs decreased or follow-up adherence improved.
What do these findings mean?
- Strengthening adherence to CRC screening across multiple stages is critical to maximizing public health and economic benefits.
- CRC screening strategies should be tailored to the adherence characteristics of target populations.
- A key limitation is the assumption of a one-time CRC screening scenario, without considering repeated screening for some strategies, making our study more relevant to settings with limited resources and suboptimal follow-up adherence.
Citation: Xie J, Dong X, Luo Z, Wang C, Zheng Y, Chen X, et al. (2025) The impact of adherence on colorectal cancer screening cost-effectiveness: A modeling study. PLoS Med 22(11): e1004807. https://doi.org/10.1371/journal.pmed.1004807
Academic Editor: Gilaad G. Kaplan, University of Calgary, CANADA
Received: April 29, 2025; Accepted: October 29, 2025; Published: November 26, 2025
Copyright: © 2025 Xie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: This study is funded by the National Natural Science Foundation of China (Grant number: 82404340, DXS), CAMS Innovation Fund for Medical Science (Grant number: 2021-I2M-1-067, DXS), nonprofit Central Research Institute (Grant number: 2019PT320027, LN), noncommunicable Chronic Diseases-National Science and Technology Major Project (Grant number: No.2024ZD0520100, DLB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: CI, confidence interval; CRC, colorectal cancer; CRC-SIM, colorectal cancer screening simulation; FIT, fecal immunochemical test; ICER, incremental cost-effectiveness ratio; mt-sDNA, multitarget stool DNA; NCSPs, national cancer screening programs; NMPAmin, minimum standards of China National Medical Products Administration; QALYs, quality-adjusted life years; RF, risk factors questionnaire; RR, relative risk; WTP, willingness-to-pay
Introduction
Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related mortality, with an estimated 1.93 million new cases and 0.90 million deaths in 2022 [1]. China experienced a substantial burden of CRC, accounting for around 30% of the global burden [2]. According to data from the National Cancer Center of China, over 517,100 new CRC cases and 240,000 deaths occurred in 2022 [2]. Although screening is effective in reducing CRC mortality [3], optimizing its implementation is essential to improving the cost-effectiveness of population-based screening programs.
Colonoscopy is the gold standard for CRC screening; however, its widespread use as a primary screening modality could be limited by resource constrained, high complications, and low adherence rates [4–6]. Regions with constrained resources consequently adopt a two-step screening process: an initial phase entails low-invasive techniques to identify high-risk populations from healthy individuals, followed by the subsequent colonoscopy [3,7]. Low-invasive tools commonly used in the initial phase include risk assessment questionnaires and fecal occult blood tests. More recently, blood- and stool-based biomarkers have emerged as promising alternatives for high-risk identification, offering potentially higher accuracy and improved uptake [8,9].
The uptake rates among initial screening and follow-up colonoscopy are important to increase screening efficacy and cost-effectiveness. Currently, the uptake rates of screening recommendations remain suboptimal; the uptake for colonoscopy among high-risk individuals ranges from 14% to 26.5% [10–13]. Non-adherence at initial risk assessment and follow-up colonoscopy could lead to missed diagnoses, delayed treatments, and reduced cost-effectiveness [14]. Moreover, an increase in uptake rate typically needs higher expenditures. Policymakers are constantly faced with the challenge of health benefits and fiscal expenditure trade-offs at varying uptake rates [15]. The expected long-term effectiveness and cost-effectiveness of screening under multilevel, especially under suboptimal adherence, and multistage adherence scenarios remain underexplored [13,16,17].
Simulation modeling studies can provide valuable guidance for optimizing CRC screening and informing guideline updates [18]. By synthesizing data on epidemiology, disease progression, intervention, and healthcare costs, these models could evaluate the simulated target scenarios and quantify the long-term efficacy and cost-effectiveness impact of various uptake rates [19–21]. However, most studies rely on simplified assumptions of optimal uptake rates (100%) or neglect the comprehensive impact of multiple-stage uptake rates across screening processes [22,23].
To address these gaps, we construct a multistate Markov model to simulate the long-term health benefits and cost-effectiveness impact of the uptake rate on CRC screening. Our findings could help policymakers optimize resource allocation and strengthen quality management in screening programs.
Materials and methods
Markov model overview
We developed and validated a Markov-based colorectal cancer screening simulation (CRC-SIM) model to simulate the natural history, screening, and clinical management of CRC adapted to the Chinese population. The Markov structure facilitates dynamic health states transitions and long-term evaluation of various “what-if” scenarios for medical intervention [24]. CRC-SIM consists of nine mutually health states: normal epithelium, non-advanced adenoma, advanced adenoma (10 mm, with villous components, or high-grade intraepithelial neoplasia), CRC stages I–IV, CRC-related death, and death from other causes (Fig 1) [25,26]. A population cohort with 100,000 hypothetical individuals was simulated, with all individuals entering the model at the age of 40 years, in alignment with the starting age of national programs under China’s Basic Public Health Service. The individuals were followed up until age 79 (the average life expectancy of the Chinese population [27]) or death, whichever occurred first [13,28]. At the start of the simulation, certain proportions of no neoplasm, non-advanced adenoma, advanced adenoma, and CRC (stage I–IV) are assigned to the hypothetical population. Each time cycle of the model (1 year), individuals have the probability of transitioning between health states. If an individual were diagnosed, the disease stops progressing, and this individual should follow a disease pathway. Screening and removal of adenomas can prevent the occurrence of CRC, while preclinical CRC will be regarded as screening-detected CRC. Individuals with a positive diagnostic colonoscopy enter a surveillance scheme. Subjects will then continue to have the probability to progress to the next states, as those without findings at screening. The detailed components and assumptions were detailed in the S1 Text.
Abbreviations: NAA, non-advanced Adenoma; AA, advanced adenoma; CRC, colorectal cancer.
Model parameters
The model parameters included transition probabilities, health utilities, and costs. Transitional probabilities for the natural history model were derived from systematic literature reviews of Chinese studies [29–32]. Data from large-scale clinical studies in China were utilized to estimate survival outcomes and adverse event rates after treatment (Table A in S1 Text) [33]. The age-specific all-cause mortality was derived from the China Statistics Yearbook. Quality-adjusted life years (QALYs) were calculated as life expectancy weighted by utility [34]. Utility scores for each health state, ranging from 0 (death) to 1 (normal epithelium), were obtained from a hospital-based cross-sectional study on 300 newly diagnosed colorectal neoplasm patients using EQ-5D-5L in China (Table 1) [35,36].
From a societal perspective, cost components include (1) direct medical expenses from clinical diagnoses and treatments; (2) travel expenses; (3) productivity losses; and (4) costs specific to screening procedures [33]. Cost estimates were sourced from population-based CRC screening programs in China, as well as a multicenter, cross-sectional survey in 37 tertiary hospitals in 13 provinces across China (Table B in S1 Text) [13,33]. All costs were adjusted based on the consumer price index in China and subsequently converted into US dollars (1 USD = 7 RMB).
To accurately simulate expected CRC epidemiology in China, parameters were calibrated to targeted data from the Global Burden of Disease database (https://www.healthdata.org/) and China’s National Cancer Center registry, including age-specific CRC incidence, mortality, and stage distribution [37,38]. Bound optimization BY quadratic approximation, a derivative-free optimization algorithm, was used to search for an optimal parameter solution by minimizing the weighted sum of squared differences between the observed and simulated data [39,40]. Model outputs were consistent with the targeted observation (Figs A and B in S1 Text).
Screening strategies and characteristics
Based on current CRC screening practices, six initial screening modalities were considered before the follow-up colonoscopy for high-risk individuals, including conventional approaches: (1) questionnaire-based risk factors assessment, (2) fecal immunochemical tests (FITs), and (3) questionnaire-based risk factors assessment combined with FIT outcomes. Additionally, non-invasive biomarker-based strategies were evaluated: (4) a hypothetical non-invasive test that meets the minimum standards established by China National Medical Products Administration (NMPA), (5) a multitarget stool DNA (mt-sDNA) test, and (6) blood-based strategies [3,7,41]. Individuals were scheduled to undergo one-time screening at age 40 across all screening modalities, aligning with current CRC screening practice in China (Table C in S1 Text). We further assumed that non-adherent individuals did not receive further screening. The sensitivity and specificity for initial screening were derived from published literature or systematic reviews on Chinese populations (Table 2) [42–45]. Baseline performance characteristics for non-invasive tests were derived from the decision criteria outlined by NMPA, with a sensitivity of 85% for CRC, 50% for advanced adenoma, and a CRC specificity of 90%. Besides, we comprehensively collected performance data for mt-sDNA and blood-based tests approved by NMPA [46–51]. The performance data were updated based on a meta-analysis of approved products (Table D in S1 Text). Follow-up colonoscopy parameters were sourced from a recent meta-analysis [4].
Cost‑effectiveness analysis
We projected CRC cases, death cases, QALYs, lifetime costs, and the incremental cost-effectiveness ratio (ICER) for each screening strategy. QALYs were calculated as life years weighted by utility. Strategies that provided fewer QALYs at a higher cost than any alternative were considered dominated; Strategies yielding more QALYs at a lower cost dominate. ICERs were calculated by dividing the incremental costs by the incremental QALYs for each strategy, compared either to no screening or to the next most effective alternative (i.e., the most cost-effective option among the remaining lower-cost strategies). A screening strategy would consider to be considered cost-effective if the ICER was less than the predefined willingness-to-pay (WTP) threshold of the 2022 per capita gross domestic product in China per QALY gained (US$12,246) [52]. Cost-effectiveness frontier was performed to identify the optimal screening strategies among the evaluated strategies. All health and cost outcomes were calculated and discounted at 5% according to the China Guidelines for Pharmacoeconomic Evaluations and with the application of half-cycle correction [34].
Uptake rate to initial screening and follow-up colonoscopy
Analyses considered 2 uptake rate scenarios: (1) In the base case analysis, an idealized implementation scenario were simulated as a reference, assuming 100% initial uptake and 100% followed-up colonoscopy rate after a positive noninvasive test; (2) Additionally, a more realistic spectrum of random uptake rates was assumed, indicating that the entire population has a probability p (ranging from 0% to 100%) of adherence to each part of the screening pathway (initial screening test and follow-up colonoscopy) independent of previous behavior, to assess the impact on the effective and cost-effectiveness of screening programs [13,42,53].
Finally, we generated a two-dimensional uptake health benefit and cost-effectiveness “heatmap” to visualize the joint effects of initial screening and follow-up colonoscopy uptake rates on prevented CRC death cases and ICER, with color gradients indicating corresponding values. This tool can assist test developers and policymakers in identifying the expected adherence thresholds for achieving cost-effective outcomes. Accordingly, cost-effectiveness non-inferiority was defined as achieving at least the same cost-effectiveness benefit as the predefined WTP threshold relative to no screening, at specific adherence levels. Cost-effectiveness noninferiority is the same as non-dominance.
Sensitivity analysis
Extensive deterministic sensitivity analysis was performed to assess the robustness of the model and identify the most sensitive parameters, including screening performance (sensitivity, specificity, and complications rate), costs (screening, examinations, treatments, follow-up), and health utility. Tornado diagrams identified the parameters with the strongest impact on the ICER in one-way sensitivity analyses. Probabilistic sensitivity analysis was performed using 10,000 Monte Carlo simulations and was used to calculate the 95% confidence intervals (CIs).
This study is reported as per the Strengthening the Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) Statement (Table E in S1 Text) [54]. The model was conducted in TreeAge 2022 R2.1 (TreeAge Software, Williamstown, MA), and all statistical analyses were performed in R (Version 4.0.3, R Foundation for Statistical Computing, Vienna, Austria).
Results
Health benefit and cost-effectiveness outcomes with uptake rates of 100%
All screening modalities were effective in reducing CRC cases and deaths and yielded QALY gains, with non-invasive biomarker-based strategies outperforming conventional approaches. Conventional methods, include questionnaire-based assessment, FIT, and combined RF-FIT strategies, prevented 154 (95% CI [124, 179]), 157 (95% CI [114, 199]), and 224 (95% CI [157, 292]) CRC cases, and 90 (95% CI [74, 104]), 102 (95% CI [76, 130]), and 151 (95% CI: [109, 195]) deaths per 100,000 persons compared with no screening. Non-invasive biomarker-based strategies, including the minimum standards of China National Medical Products Administration ([NMPAmin], blood-based, and mt-sDNA tests, prevented 312 (95% CI [257, 360]), 383 (95% CI [259, 493]), and 384 (95% CI [312, 441]) CRC cases, and 210 (95% CI [175, 241]), 253 (95% CI [174, 324]), and 255 (95% CI [208, 290]) deaths per 100,000 persons, respectively, compared with no screening (Table 3).
The average lifetime cost per person was $199.410 (95% CI [194.149, 204.835]) without screening (Table 3). For questionaries, FIT, and their combinations, the projected lifetime costs are $222.249 (95% CI [210.465, 239.49]), $218.209 (95% CI [206.503, 235.509]), and $227.127 (95% CI [214.45, 242.739]), while costs increased for NMPAmin, blood-based, and mt-sDNA strategies, reaching $356.775 (95% CI [302.475, 424.675]), $353.821 (95% CI [298.343, 419.959]), and $353.165 (95% CI [299.055, 420.937]) per person. All strategies were cost-effective versus no screening at the $12,246/QALY threshold, yet non-invasive biomarker-based strategies were suboptimal compared to questionnaire-based assessment combined with FIT (the most cost-effective strategy) (ICERs: $14,321) (Figs C and D in S1 Text).
Impact of uptake rates on screening health benefit and cost-effectiveness
The health benefit and cost-effectiveness heatmap was developed to quantify the effect of multistep uptake rate on CRC screening (Figs 2 and E in S1 Text). Briefly, higher uptake rates of initial screening and follow-up colonoscopy could substantially prevent more CRC deaths and improve cost-effectiveness across all screening strategies (ICER decreased). For questionnaire-based assessment combined with the FIT strategy, the ICER decreased substantially as uptake improved, ranging from $86,838 per QALY gained with 10% uptake for initial screening and 10% follow-up colonoscopy to $2,044 per QALY gained with 100% uptake for both. For non-invasive screening modalities, e.g., NMPAmin, ICER decreased from $142,433 to $8,677 per QALY as the uptake rates increased.
The following initial screening strategies were considered, including questionnaire-based risk factors assessment (RF), FIT, questionnaire-based risk factors assessment combined with FIT outcomes (RF-FIT), a hypothetical non-invasive test that meets the minimum criteria set by China National Medical Products Administration (NMPAmin), blood-based strategies, and mt-sDNA test. The incremental cost-effectiveness ratio (ICER, $/QALY gain) was calculated for each screening modality in comparison to no screening, accounting for the uptake rates for both initial screening and follow-up colonoscopy. The black line indicates the willingness-to-pay threshold ($12,246 per QALY gained); ICERs below this value are considered cost-effective. Red represents ICERs above the willingness-to-pay threshold, with darker shades corresponding to higher ICERs and lower cost-effectiveness; blue represents ICERs below the threshold, with darker shades corresponding to lower ICERs and higher cost-effectiveness. Abbreviations: ICER, incremental cost-effectiveness ratio; FIT, fecal immunochemical test; NMPA, China National Medical Products Administration; mt-sDNA, multitarget stool DNA.
Among all strategies, the questionnaire-based assessment combined with the FIT strategy reaches the lowest colonoscopy uptake threshold to be cost-effective under identical initial screening uptake. Setting the initial uptake rate as 10%, at least a 70% uptake rate of follow-up colonoscopy was needed to achieve cost-effectiveness at the WTP threshold of $12,246 per QALY gained. However, it is not cost-effective for the hypothetical non-invasive screening tests that meet the minimum criteria set by NMPA compared with no screening, even if all individuals with abnormal results adhered to the follow-up colonoscopy (NMPAmin ICER: $13,978 per QALY gained).
For non-invasive biomarker-based initial modalities, a higher colonoscopy uptake rate was critical to ensure the equivalent cost-effectiveness done by traditional strategies (cost-effectiveness noninferiority at 70% for NMPAmin, 50% for blood-based tests, and 50% for mt-sDNA under the WTP threshold), irrespective of improvements in initial screening uptake. Furthermore, the adherence to colonoscopy also magnifies the benefits of improving the initial uptake rate. For example, the RF-FIT strategy achieved cost-effectiveness (ICER: $11,293/QALY) by maintaining a 70% colonoscopy uptake rate, requiring only a 10% initial screening rate. However, if colonoscopy uptake drops to 10%, initial screening uptake must exceed 90% to achieve an ICER of $15,934/QALY.
Threshold analysis for cost and uptake rate
Threshold analysis indicates that reducing the cost or increasing the follow-up uptake for the non-invasive biomarker-based tests would improve overall cost-effectiveness (Figs F and G in S1 Text). The threshold cost of non-invasive biomarker-based tests for initial screening, e.g., mt-sDNA strategies, should decrease to $131.7 to be cost-effective compared to RF-FIT. Additionally, the follow-up colonoscopy uptake rate must be higher to be cost-effective to accommodate the non-invasive biomarker-based tests. With a 100% initial uptake rate, the NMPAmin strategy would require a follow-up colonoscopy rate of at least 80%−90% to meet the cost-effectiveness threshold, compared to 20%−30% for RF-FIT strategies (current reference value in China).
Sensitivity analysis
Extensive sensitivity analyses showed that the model was robust across a broad range of parameter values. Tornado diagrams showed the parameters with the greatest influence on the ICER (Fig 3). The model’s cost-effectiveness was most sensitive to the specificity and sensitivity of AA and NAA, health utility values of AA and CRC, and risk assessment specificity and cost.
Six different initial screening modalities were considered, including questionnaire-based risk factors assessment (RF), FIT, questionnaire-based risk factors assessment combined with FIT outcomes (RF-FIT), a hypothetical non-invasive test that meets the minimum criteria set by China National Medical Products Administration (NMPAmin), blood-based strategies, and mt-sDNA test. The top five parameters with the greatest impact on the incremental cost-effectiveness ratio (ICER) are displayed. Costs are expressed in US dollars. The length of the blue bar represents the extent of ICER variation when the corresponding parameter changes. The cost-effectiveness threshold was set at $12,246 per QALY gained. Abbreviations: ICER, incremental cost-effectiveness ratio; QALYs, quality-adjusted life-years; NAA, non-advanced adenoma; AA, advanced Adenoma; CRC, colorectal cancer.
Discussions
Globally, quality improvement in adherence is increasingly recognized as essential for optimizing CRC programs. However, there remains no comprehensive analysis of multistep uptake impacts across the different screening stages based on China’s real-world data. Using a validated Markov model, we demonstrate that enhancing uptake at both initial risk assessment and colonoscopy stages yields significant cost-effectiveness. Furthermore, we quantified how enhanced uptake rates and colonoscopy follow-up translate into health and economic outcomes, providing evidence-based guidance for resource allocation and optimizing CRC screening programs.
Adherence is a key determinant of screening effectiveness and cost-effectiveness [55,56]. A population-based study showed that poor follow-up adherence diminished the mortality benefits of CRC screening [57]. A modeling study further demonstrated that increasing colonoscopy adherence after positive blood tests from 60% to 80% improved QALYs gains from 38–70 to 66–93 [56]. However, previous studies focused mainly on single-stage uptake, overlooking cumulative multistage adherence impacts [56,57]. We found that improving uptake rates across multiple stages, including high-risk identification and colonoscopy, yields nonlinear health gains that outpace incremental costs, thereby improving cost-effectiveness. This disproportionate benefit is driven in part by economies of scale in outreach, risk stratification, and system infrastructure. To support policy decisions, we developed a quantitative uptake-rate heatmap, identifying minimum uptake thresholds across multiple stages for cost-effective delivery, offering a practical tool for program design and quality monitoring.
The lack of head-to-head comparisons in pivotal studies limits direct assessment of competing strategies. Our evaluation of six primary screening approaches in China identified the questionnaire combined with the FIT strategy as the most cost-effective long-term option. This finding is consistent with population-based evidence that integrating risk assessment with FIT enhances colonoscopy uptake and overall screening efficiency [13,58,59]. While non-invasive biomarker approaches underperform conventional methods in cost-effectiveness, they remain better than no screening in cost-effectiveness, aligning with previous evaluations in diverse health settings [56,60,61]. These results indicate that biomarker-based screening potentially serves as a viable alternative for individuals unwilling to undergo conventional testing, potentially improving screening coverage. Diversifying screening options based on population preferences is, therefore, crucial for optimizing public health outcomes.
Non-invasive biomarker-based screening, while promising, continues to face challenges in efficiency, cost, and adherence. A modeling study in the US estimated that blood-based tests would require a 20-fold reduction in cost to be cost-effective compared to FIT [56]. In China, we identified a cost-effectiveness threshold of $131.7, slightly lower than the threshold in US modeling studies ($140 per-test) [61]. This disparity likely reflects differences in disease burden, cost structures, or WTP thresholds. Nevertheless, the threshold remains economically prohibitive in the Chinese context, where colonoscopy costs approximately $73, highlighting a significant barrier to widespread adoption, especially for individuals without health insurance. Future reductions in cost through economies of scale and localized manufacturing may improve the feasibility in resource-limited countries like China.
Failure to complete follow-up colonoscopy increases CRC mortality up to 3-fold [62], yet rates of follow-up colonoscopy for abnormal (non-colonoscopy) screening tests vary from 30% to 82% in screening trials. Our analysis showed that biomarker-based strategies require consistently higher follow-up colonoscopy rates (at least 50%) to achieve cost-effectiveness, compared to the RF-FIT strategy. This reflects the limited benefit of high-cost tests without follow-up colonoscopy completion. For current screening programs in China, prioritizing targeted adherence interventions over expensive non-invasive tests may represent a more efficient allocation of limited resources.
Achieving higher uptake rates will require considerable improvements in patient adherence and healthcare system capacity. Current uptake in China remains suboptimal due to resource disparities, awareness gaps, and logistical barriers, far below the U.S. National Colorectal Cancer Roundtable’s 80% target Europeans’ minimum acceptable threshold of 45% and recommends a threshold of at least 65% [63,64]. Closing this gap requires collaborative financial incentives among health systems and payers. Evidence suggests that outreach, navigation, education of patients or providers, reminders, and financial incentives can improve screening benefits [65]. Meta-analysis further validated navigation (RR = 2.01) and outreach (RR = 2.26) were the most effective interventions and could increase screening rates by approximately 20% [66]. Furthermore, expanding uptake metrics beyond single-round uptake to multiround participation and completion rates is crucial to ensure comprehensive program evaluation [67].
Currently, many countries recommend repeated CRC screening at regular intervals [3,11]. Our model primarily addressed the government-funded, one-time screening widely adopted in countries with limited screening resources and suboptimal follow-up adherence [68–70], which may underestimate the long-term effectiveness of repeated screening. Nevertheless, one-time screening reflects a pragmatic reality in many middle- and low-income countries, where broader coverage may yield greater population-level benefit than repeated screening of targeted individuals [71]. Four national cancer screening programs (NCSPs) are funded by the Chinese government (Table F in S1 Text), among which one-time screening is provided by most areas of the NCSPs [71]. Furthermore, evidence supporting the adoption of repeated screening remains limited in these settings. Evidence from some high-income countries also highlights suboptimal adherence to repeated screening: in the USA, only 38%–49% of participants with an initial negative result undergo repeat screening [72]. Therefore, optimizing strategies that ensure the delivery of at least one effective screening offer a practical complement to existing repeated screening paradigms.
An important strength is that our study comprehensively analyzes the impact of uptake rate in real-world scenarios across multistage (risk-assessment and follow-up colonoscopy) and a wide range of levels (10%–100%) for each screening strategy. We proposed a quantitative uptake rate heatmap to guide policy implementation. Referring to the threshold heatmap, policymakers can estimate the budgetary impact and incremental resource requirements for CRC implementation. Moreover, blood-based strategies have gradually become a promising technique for cancer screening for the advantage of lower hazards and higher compliance. Our study comprehensively evaluated the cost-effectiveness of non-invasive biomarker approaches, including mt-sDNA and blood-based test products approved by NMPA, providing valuable evidence to support the development, pricing, and implementation of new CRC screening methods.
Our study has several limitations. First, our model assumes all CRC cases arise from the adenoma-carcinoma sequence (given that the majority of CRC follow the adenoma-carcinoma pathway), for lack of natural history data of serrated polyps [73]. Besides, while we have endeavored to select high-quality epidemiological and screening parameters from China, population-based research on non-invasive screening methods is still relatively limited. Consequently, we conducted a systematic review of CRC screening products currently available in China to evaluate their effectiveness. As new technologies and revised guidelines emerge, it is essential to re-examine the definitions of current screening practices and update the analysis as necessary. Third, these thresholds are context-specific to China’s healthcare system and may not be directly generalizable to other settings due to differences in economic conditions, healthcare infrastructure. Caution should be exercised when interpreting these results across different health systems. Fourth, our model applied a life expectancy of 79 years, which may underestimate the long-term benefits of screening, as it does not capture individuals who survive beyond this age as a result of screening. Modeling a longer life span is constrained by the lack of detailed age-specific mortality data for those over 80; future updates could address this limitation as more robust data become available. Additionally, the one-time screening assumption, while limiting generalizability to settings with repeated screening programs, provides a realistic framework for areas with limited resources and suboptimal adherence [71]. Future model-based studies should also incorporate repeated screening evaluations as healthcare capacity expands and more evidence is available.
Our decision analysis demonstrated that multistage adherence is a key determinant of the long-term effectiveness and cost-efficiency of CRC screening. While questionnaire-based risk factors assessment combined with FIT is cost-effective under current conditions, optimal strategy selection should be guided by real-world adherence. Non-invasive screening methods may become viable with lower costs and higher follow-up compliance. This study provides strategic insight to strengthen the comprehensive management of CRC screening, contributing to global efforts toward CRC elimination, especially in countries with limited resources.
Supporting information
S1 Text.
Table A. Nature history parameter of colorectal cancer. Table B. Cost of different screening strategies. Table C. Colorectal cancer screening guideline in China. Table D. Parameters review of multitarget stool DNA- or blood-based screening products approved by NMPA in China. Table E. CHEERS 2022 Checklist. Table F. Characteristics of the four national cancer screening programs in China. Fig A. Comparison between estimated and observed age-specific incidence and mortality of colorectal cancer. Fig B. Comparison between observed and model-calibrated stage distribution of colorectal cancer at diagnosis. Fig C. Cost-effectiveness plane for colorectal cancer screening strategies. Fig D. Cost-effectiveness acceptability curve of different screening strategies for colorectal cancer screening. Fig E. Impact of uptake rate on preventable colorectal cancer death of various colorectal cancer screening strategies. Fig F. Sensitivity analysis of the cost threshold of the biomarker-based screening test compared with the most cost-effective strategy. Fig G. Sensitivity analysis of the colonoscopy uptake rate threshold for the biomarker-based screening test compared with the most cost-effective strategy (RF-FIT strategy).
https://doi.org/10.1371/journal.pmed.1004807.s001
(DOCX)
References
- 1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. pmid:33538338
- 2. Han B, Zheng R, Zeng H, Wang S, Sun K, Chen R, et al. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent. 2024;4(1):47–53. pmid:39036382
- 3. Shaukat A, Kahi CJ, Burke CA, Rabeneck L, Sauer BG, Rex DK. ACG clinical guidelines: colorectal cancer screening 2021. Am J Gastroenterol. 2021;116(3):458–79. pmid:33657038
- 4. Knudsen AB, Rutter CM, Peterse EFP, Lietz AP, Seguin CL, Meester RGS, et al. Colorectal cancer screening: an updated modeling study for the US Preventive Services Task Force. JAMA. 2021;325(19):1998–2011. pmid:34003219
- 5. Verma M, Sarfaty M, Brooks D, Wender RC. Population-based programs for increasing colorectal cancer screening in the United States. CA Cancer J Clin. 2015;65(6):497–510. pmid:26331705
- 6. Brenner H, Heisser T, Cardoso R, Hoffmeister M. Reduction in colorectal cancer incidence by screening endoscopy. Nat Rev Gastroenterol Hepatol. 2024;21(2):125–33. pmid:37794234
- 7. Chinese Society of Gastroenterology, Cancer Collaboration Group of Chinese Society of Gastroenterology, Chinese Medical Association. Chinese consensus on prevention of colorectal neoplasia (2021, Shanghai). J Dig Dis. 2022;23(2):58–90. pmid:34984819
- 8. Sung JJY, Ng SC, Chan FKL, Chiu HM, Kim HS, Matsuda T, et al. An updated Asia Pacific Consensus Recommendations on colorectal cancer screening. Gut. 2015;64(1):121–32. pmid:24647008
- 9. Broccard SP, Kasbi AA, Bagaria SP, Jones J, Shoudry M, Gabriel EM. Liquid biopsies for colorectal cancer: a narrative review of ongoing clinical trials and the current use of this technology at a comprehensive cancer center. J Gastrointest Oncol. 2022;13(1):438–49. pmid:35284120
- 10. Chen H, Li N, Ren J, Feng X, Lyu Z, Wei L, et al. Participation and yield of a population-based colorectal cancer screening programme in China. Gut. 2019;68(8):1450–7. pmid:30377193
- 11. US Preventive Services Task Force, Davidson KW, Barry MJ, Mangione CM, Cabana M, Caughey AB, et al. Screening for colorectal cancer: US Preventive Services Task Force Recommendation Statement. JAMA. 2021;325(19):1965–77. pmid:34003218
- 12. The Lancet Gastroenterology Hepatology. Improving uptake of colorectal cancer screening. Lancet Gastroenterol Hepatol. 2017;2(11):767. pmid:29017706
- 13. Dong X, Du L, Luo Z, Xu Y, Wang C, Wang F, et al. Combining fecal immunochemical testing and questionnaire-based risk assessment in selecting participants for colonoscopy screening in the Chinese National Colorectal Cancer Screening Programs: a population-based cohort study. PLoS Med. 2024;21(2):e1004340. pmid:38386617
- 14. Wang F, Dong X, Li N. A call to improve the quality of screening programs. Nat Med. 2023;29(5):1062–3. pmid:37106169
- 15. Romero Y, Trapani D, Johnson S, Tittenbrun Z, Given L, Hohman K, et al. National cancer control plans: a global analysis. Lancet Oncol. 2018;19(10):e546–55. pmid:30268693
- 16. Kaminski MF, Robertson DJ, Senore C, Rex DK. Optimizing the quality of colorectal cancer screening worldwide. Gastroenterology. 2020;158(2):404–17. pmid:31759062
- 17. Lee MJ, Hueniken K, Kuehne N, Lu L, Jiang SX, Id Said B, et al. Cancer patient-reported preferences and knowledge for liquid biopsies and blood biomarkers at a comprehensive cancer center. Cancer Manag Res. 2020;12:1163–73. pmid:32104096
- 18. Lin JS, Perdue LA, Henrikson NB, Bean SI, Blasi PR. Screening for colorectal cancer: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA. 2021;325(19):1978–98. pmid:34003220
- 19. McMahon PM, Kong CY, Johnson BE, Weinstein MC, Weeks JC, Tramontano AC, et al. Chapter 9: the MGH-HMS lung cancer policy model: tobacco control versus screening. Risk Anal. 2012;32 Suppl 1(Suppl 1):S117-24. pmid:22882882
- 20. Criss SD, Sheehan DF, Palazzo L, Kong CY. Population impact of lung cancer screening in the United States: projections from a microsimulation model. PLoS Med. 2018;15(2):e1002506. pmid:29415013
- 21. Criss SD, Cao P, Bastani M, Ten Haaf K, Chen Y, Sheehan DF, et al. Cost-effectiveness analysis of lung cancer screening in the United States: a comparative modeling study. Ann Intern Med. 2019;171(11):796–804. pmid:31683314
- 22. Benito L, Travier N, Binefa G, Vidal C, Espinosa J, Milà N, et al. Longitudinal adherence to immunochemical fecal occult blood testing vs guaiac-based FOBT in an organized colorectal cancer screening program. Cancer Prev Res (Phila). 2019;12(5):327–34. pmid:30890542
- 23. Pedersen K, Kristiansen IS, Sy S, Kim JJ, Burger EA. Designing guidelines for those who do not follow them: the impact of adherence assumptions on optimal screening guidelines. Value Health. 2023;26(8):1217–24. pmid:37116697
- 24. Xia C, Chen W. The NCC mathematical modeling framework for decision-making of six major cancers. J Natl Cancer Cent. 2022;3(1):35–47. pmid:39036317
- 25. Heisser T, Hoffmeister M, Brenner H. Effects of screening for colorectal cancer: development, documentation and validation of a multistate Markov model. Int J Cancer. 2021;148(8):1973–81. pmid:33320964
- 26. Chen Q, Fan Y, Huang K, Li W, Geldsetzer P, Bärnighausen T, et al. Cost-effectiveness of population-based screening for chronic obstructive pulmonary disease in China: a simulation modeling study. Lancet Reg Health West Pac. 2024;46:101065. pmid:38721063
- 27. Talifu Z, Chen C, Shen X, Zhong P, Luo Y, Su B. Age, sex, and disease-specific mortality contributions to life expectancy in urban and rural China, 1987-2021. China CDC Wkly. 2024;6(28):684–8. pmid:39035873
- 28.
National Bureau of Statistics of China. China Statistical Yearbook 2020. Beijing, China: China Statistics Press; 2021.
- 29. Li ZF, Huang HY, Shi JF, Guo CG, Zou SM, Liu CC, et al. A systematic review of worldwide natural history models of colorectal cancer: classification, transition rate and a recommendation for developing Chinese population-specific model. Zhonghua Liu Xing Bing Xue Za Zhi. 2017;38(2):253–60. pmid:28231677
- 30. Wang R, Lian J, Wang X, Pang X, Xu B, Tang S, et al. Survival rate of colorectal cancer in China: a systematic review and meta-analysis. Front Oncol. 2023;13:1033154. pmid:36937415
- 31. Pickhardt PJ, Hassan C, Laghi A, Zullo A, Kim DH, Morini S. Cost-effectiveness of colorectal cancer screening with computed tomography colonography: the impact of not reporting diminutive lesions. Cancer. 2007;109(11):2213–21. pmid:17455218
- 32. Areia M, Mori Y, Correale L, Repici A, Bretthauer M, Sharma P, et al. Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study. Lancet Digit Health. 2022;4(6):e436–44. pmid:35430151
- 33. Xia C, Xu Y, Li H, He S, Chen W. Benefits and harms of polygenic risk scores in organised cancer screening programmes: a cost-effectiveness analysis. Lancet Reg Health West Pac. 2024;44:101012. pmid:38304718
- 34. Whitehead SJ, Ali S. Health outcomes in economic evaluation: the QALY and utilities. Br Med Bull. 2010;96:5–21. pmid:21037243
- 35. Huang W, Yang J, Liu Y, Liu C, Zhang X, Fu W, et al. Assessing health-related quality of life of patients with colorectal cancer using EQ-5D-5L: a cross-sectional study in Heilongjiang of China. BMJ Open. 2018;8(12):e022711. pmid:30530472
- 36. Cheng C-Y, Calderazzo S, Schramm C, Schlander M. Modeling the natural history and screening effects of colorectal cancer using both adenoma and serrated neoplasia pathways: the development, calibration, and validation of a discrete event simulation model. MDM Policy Pract. 2023;8(1):23814683221145701. pmid:36698854
- 37. GBD 2021 Diseases and Injuries Collaborators. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2133–61. pmid:38642570
- 38. Zeng H, Ran X, An L, Zheng R, Zhang S, Ji JS, et al. Disparities in stage at diagnosis for five common cancers in China: a multicentre, hospital-based, observational study. Lancet Public Health. 2021;6(12):e877–87. pmid:34838194
- 39. Kong CY, McMahon PM, Gazelle GS. Calibration of disease simulation model using an engineering approach. Value Health. 2009;12(4):521–9. pmid:19900254
- 40.
Powell MJ. The BOBYQA algorithm for bound constrained optimization without derivatives. NA2009/06. Cambridge: University of Cambridge; 2009.
- 41. Schroy PC 3rd, Wong JB, O’Brien MJ, Chen CA, Griffith JL. A risk prediction index for advanced colorectal neoplasia at screening colonoscopy. Am J Gastroenterol. 2015;110(7):1062–71. pmid:26010311
- 42. Dong X, Du L, Luo Z, Xu Y, Wang C, Wang F, et al. Combining fecal immunochemical testing and questionnaire-based risk assessment in selecting participants for colonoscopy screening in the Chinese National Colorectal Cancer Screening Programs: a population-based cohort study. PLoS Med. 2024;21(2):e1004340. pmid:38386617
- 43. Zhou Q, Li H-L, Li Y, Gu Y-T, Liang Y-R, Liu H-Z, et al. Cost-effectiveness analysis of different screening strategies for colorectal cancer in Guangzhou, southern China: a Markov simulation analysis based on natural community screening results. BMJ Open. 2021;11(9):e049581. pmid:34489283
- 44. Li Q, Yu L, Xue F, Ma W, Yao K. Evaluation of optimized sequential screening program of colorectal cancer in current China. Zhonghua Yu Fang Yi Xue Za Zhi. 2014;48(11):995–1000. pmid:25582372
- 45. Cenin D, Li P, Wang J, de Jonge L, Yan B, Tao S, et al. Optimising colorectal cancer screening in Shanghai, China: a modelling study. BMJ Open. 2022;12(5):e048156. pmid:35577474
- 46. Hu Y-T, Chen X-F, Zhai C-B, Yu X-T, Liu G, Xiong Z-G, et al. Clinical evaluation of a multitarget fecal immunochemical test-sDNA test for colorectal cancer screening in a high-risk population: a prospective, multicenter clinical study. MedComm (2020). 2023;4(4):e345. pmid:37576863
- 47. Wang J, Liu S, Wang H, Zheng L, Zhou C, Li G, et al. Robust performance of a novel stool DNA test of methylated SDC2 for colorectal cancer detection: a multicenter clinical study. Clin Epigenetics. 2020;12(1):162. pmid:33126908
- 48. Fang Y, Peng J, Li Z, Jiang R, Lin Y, Shi Y. Identification of multi-omic biomarkers from fecal DNA for improved detection of colorectal cancer and precancerous lesions. MedRxiv. 2022.
- 49. Wang Z, Shang J, Zhang G, Kong L, Zhang F, Guo Y, et al. Evaluating the clinical performance of a dual-target stool DNA test for colorectal cancer detection. J Mol Diagn. 2022;24(2):131–43. pmid:34890778
- 50. Cai G, Cai M, Feng Z, Liu R, Liang L, Zhou P, et al. A multilocus blood-based assay targeting circulating tumor DNA methylation enables early detection and early relapse prediction of colorectal cancer. Gastroenterology. 2021;161(6):2053-2056.e2. pmid:34487783
- 51. Mo S, Dai W, Wang H, Lan X, Ma C, Su Z, et al. Early detection and prognosis prediction for colorectal cancer by circulating tumour DNA methylation haplotypes: a multicentre cohort study. EClinicalMedicine. 2022;55:101717. pmid:36386039
- 52. Cai D, Shi S, Jiang S, Si L, Wu J, Jiang Y. Estimation of the cost-effective threshold of a quality-adjusted life year in China based on the value of statistical life. Eur J Health Econ. 2022;23(4):607–15. pmid:34655364
- 53. Chen H, Lu M, Liu C, Zou S, Du L, Liao X, et al. Comparative evaluation of participation and diagnostic yield of colonoscopy vs fecal immunochemical test vs risk-adapted screening in colorectal cancer screening: interim analysis of a multicenter randomized controlled trial (TARGET-C). Am J Gastroenterol. 2020;115(8):1264–74. pmid:32282342
- 54. Husereau D, Drummond M, Augustovski F, de Bekker-Grob E, Briggs AH, Carswell C, et al. Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. BMC Med. 2022;20(1):23. pmid:35022047
- 55. Mohl JT, Ciemins EL, Miller-Wilson L-A, Gillen A, Luo R, Colangelo F. Rates of follow-up colonoscopy after a positive stool-based screening test result for colorectal cancer among health care organizations in the US, 2017-2020. JAMA Netw Open. 2023;6(1):e2251384. pmid:36652246
- 56. van den Puttelaar R, Nascimento de Lima P, Knudsen AB, Rutter CM, Kuntz KM, de Jonge L, et al. Effectiveness and cost-effectiveness of colorectal cancer screening with a blood test that meets the centers for medicare & medicaid services coverage decision. Gastroenterology. 2024;167(2):368–77. pmid:38552671
- 57. Ladabaum U, Mannalithara A, Schoen RE, Dominitz JA, Lieberman D. Projected impact and cost-effectiveness of novel molecular blood-based or stool-based screening tests for colorectal cancer. Ann Intern Med. 2024;177(12):1610–20. pmid:39467291
- 58. Stegeman I, de Wijkerslooth TR, Stoop EM, van Leerdam ME, Dekker E, van Ballegooijen M, et al. Combining risk factors with faecal immunochemical test outcome for selecting CRC screenees for colonoscopy. Gut. 2014;63(3):466–71. pmid:23964098
- 59. Auge JM, Pellise M, Escudero JM, Hernandez C, Andreu M, Grau J, et al. Risk stratification for advanced colorectal neoplasia according to fecal hemoglobin concentration in a colorectal cancer screening program. Gastroenterology. 2014;147(3):628-636.e1. pmid:24937264
- 60. Aziz Z, Wagner S, Agyekum A, Pumpalova YS, Prest M, Lim F, et al. Cost-effectiveness of liquid biopsy for colorectal cancer screening in patients who are unscreened. JAMA Netw Open. 2023;6(11):e2343392. pmid:37971743
- 61. Ladabaum U, Mannalithara A, Weng Y, Schoen RE, Dominitz JA, Desai M, et al. Comparative effectiveness and cost-effectiveness of colorectal cancer screening with blood-based biomarkers (liquid biopsy) vs fecal tests or colonoscopy. Gastroenterology. 2024;167(2):378–91. pmid:38552670
- 62. Lee YC, Fann JC, Chiang TH, Chuang SL, Chen SL, Chiu HM, et al. Time to colonoscopy and risk of colorectal cancer in patients with positive results from fecal immunochemical tests. Clin Gastroenterol Hepatol. 2019;17(7):1332–40.e3.
- 63. Wender R, Brooks D, Sharpe K, Doroshenk M. The National Colorectal Cancer Roundtable: past performance, current and future goals. Gastrointest Endosc Clin N Am. 2020;30(3):499–509. pmid:32439084
- 64. von Karsa L, Patnick J, Segnan N. European guidelines for quality assurance in colorectal cancer screening and diagnosis. First Edition—executive summary. Endoscopy. 2012;44 Suppl 3:SE1-8. pmid:23012113
- 65. European Colorectal Cancer Screening Guidelines Working Group, von Karsa L, Patnick J, Segnan N, Atkin W, Halloran S, et al. European guidelines for quality assurance in colorectal cancer screening and diagnosis: overview and introduction to the full supplement publication. Endoscopy. 2013;45(1):51–9. pmid:23212726
- 66. Dougherty MK, Brenner AT, Crockett SD, Gupta S, Wheeler SB, Coker-Schwimmer M, et al. Evaluation of interventions intended to increase colorectal cancer screening rates in the United States: a systematic review and meta-analysis. JAMA Intern Med. 2018;178(12):1645–58. pmid:30326005
- 67. Sadeghi S, Barzi A. USPSTF recommendations and colorectal cancer in younger adults-current challenges and future opportunities. JAMA Netw Open. 2024;7(10):e2436305. pmid:39361287
- 68. Li N, Tan F, Chen W, Dai M, Wang F, Shen S, et al. One-off low-dose CT for lung cancer screening in China: a multicentre, population-based, prospective cohort study. Lancet Respir Med. 2022;10(4):378–91. pmid:35276087
- 69. Chen R, Liu Y, Song G, Li B, Zhao D, Hua Z, et al. Effectiveness of one-time endoscopic screening programme in prevention of upper gastrointestinal cancer in China: a multicentre population-based cohort study. Gut. 2021;70(2):251–60. pmid:32241902
- 70. Wei W-Q, Chen Z-F, He Y-T, Feng H, Hou J, Lin D-M, et al. Long-term follow-up of a community assignment, one-time endoscopic screening study of esophageal cancer in China. J Clin Oncol. 2015;33(17):1951–7. pmid:25940715
- 71. Xia C, Basu P, Kramer BS, Li H, Qu C, Yu XQ, et al. Cancer screening in China: a steep road from evidence to implementation. Lancet Public Health. 2023;8(12):e996–1005. pmid:38000379
- 72. Gupta H, Husa RA, Wendt SJ, Vita A, Boone C, Weiss JB, et al. Longitudinal retrospective study of real-world adherence to colorectal cancer screening before and after the COVID-19 pandemic in the USA. BMJ Public Health. 2025;3(1):e001734. pmid:40099138
- 73. Lu B, Wang L, Lu M, Zhang Y, Cai J, Luo C, et al. Microsimulation Model for Prevention and Intervention of Coloretal Cancer in China (MIMIC-CRC): development, calibration, validation, and application. Front Oncol. 2022;12:883401. pmid:35530306